Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations18594
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory429.0 B

Variable types

Text1
Numeric9
Categorical3
DateTime2

Alerts

Amount_Funded_By_Lender is highly overall correlated with Lender_portion_Funded and 3 other fieldsHigh correlation
Lender_portion_Funded is highly overall correlated with Amount_Funded_By_Lender and 2 other fieldsHigh correlation
Lender_portion_to_be_repaid is highly overall correlated with Amount_Funded_By_Lender and 3 other fieldsHigh correlation
Total_Amount is highly overall correlated with Amount_Funded_By_Lender and 3 other fieldsHigh correlation
Total_Amount_to_Repay is highly overall correlated with Amount_Funded_By_Lender and 3 other fieldsHigh correlation
country_id is highly overall correlated with Lender_portion_Funded and 4 other fieldsHigh correlation
customer_id is highly overall correlated with country_id and 2 other fieldsHigh correlation
duration is highly overall correlated with loan_typeHigh correlation
lender_id is highly overall correlated with country_id and 2 other fieldsHigh correlation
loan_type is highly overall correlated with Total_Amount and 4 other fieldsHigh correlation
tbl_loan_id is highly overall correlated with country_id and 1 other fieldsHigh correlation
loan_type is highly imbalanced (69.0%) Imbalance
New_versus_Repeat is highly imbalanced (92.5%) Imbalance
Total_Amount is highly skewed (γ1 = 112.8584063) Skewed
Total_Amount_to_Repay is highly skewed (γ1 = 116.5174333) Skewed
Amount_Funded_By_Lender is highly skewed (γ1 = 20.79293646) Skewed
ID has unique values Unique
Amount_Funded_By_Lender has 1868 (10.0%) zeros Zeros
Lender_portion_Funded has 1868 (10.0%) zeros Zeros
Lender_portion_to_be_repaid has 1953 (10.5%) zeros Zeros

Reproduction

Analysis started2025-01-06 20:11:38.863057
Analysis finished2025-01-06 20:11:57.341448
Duration18.48 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

ID
Text

Unique 

Distinct18594
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-01-06T20:11:57.864414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.990212
Min length19

Characters and Unicode

Total characters390292
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18594 ?
Unique (%)100.0%

Sample

1st rowID_269404226088267278
2nd rowID_255356300042267278
3rd rowID_257026243764267278
4th rowID_264617299409267278
5th rowID_247613296713267278
ValueCountFrequency (%)
id_269404226088267278 1
 
< 0.1%
id_250225217173267278 1
 
< 0.1%
id_271847294122267278 1
 
< 0.1%
id_308399367770267278 1
 
< 0.1%
id_253278278418267278 1
 
< 0.1%
id_260080290274267278 1
 
< 0.1%
id_256877248892267278 1
 
< 0.1%
id_297079364851297183 1
 
< 0.1%
id_260008268836267278 1
 
< 0.1%
id_253042249843267278 1
 
< 0.1%
Other values (18584) 18584
99.9%
2025-01-06T20:11:58.630447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 81767
21.0%
7 47571
12.2%
6 41266
10.6%
8 32878
8.4%
5 24933
 
6.4%
4 24416
 
6.3%
3 23770
 
6.1%
9 23029
 
5.9%
I 18594
 
4.8%
D 18594
 
4.8%
Other values (3) 53474
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 390292
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 81767
21.0%
7 47571
12.2%
6 41266
10.6%
8 32878
8.4%
5 24933
 
6.4%
4 24416
 
6.3%
3 23770
 
6.1%
9 23029
 
5.9%
I 18594
 
4.8%
D 18594
 
4.8%
Other values (3) 53474
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 390292
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 81767
21.0%
7 47571
12.2%
6 41266
10.6%
8 32878
8.4%
5 24933
 
6.4%
4 24416
 
6.3%
3 23770
 
6.1%
9 23029
 
5.9%
I 18594
 
4.8%
D 18594
 
4.8%
Other values (3) 53474
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 390292
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 81767
21.0%
7 47571
12.2%
6 41266
10.6%
8 32878
8.4%
5 24933
 
6.4%
4 24416
 
6.3%
3 23770
 
6.1%
9 23029
 
5.9%
I 18594
 
4.8%
D 18594
 
4.8%
Other values (3) 53474
13.7%

customer_id
Real number (ℝ)

High correlation 

Distinct4962
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262489.51
Minimum6083
Maximum312696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:11:58.839643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6083
5-th percentile241751
Q1250357
median259107
Q3270051.25
95-th percentile297741
Maximum312696
Range306613
Interquartile range (IQR)19694.25

Descriptive statistics

Standard deviation28957.313
Coefficient of variation (CV)0.11031798
Kurtosis28.539836
Mean262489.51
Median Absolute Deviation (MAD)9524
Skewness-3.6393518
Sum4.8807299 × 109
Variance8.3852597 × 108
MonotonicityNot monotonic
2025-01-06T20:11:59.088944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
296718 60
 
0.3%
296758 55
 
0.3%
296992 47
 
0.3%
297596 46
 
0.2%
296562 46
 
0.2%
296745 45
 
0.2%
297741 44
 
0.2%
247613 44
 
0.2%
296998 43
 
0.2%
296287 43
 
0.2%
Other values (4952) 18121
97.5%
ValueCountFrequency (%)
6083 2
 
< 0.1%
7154 4
 
< 0.1%
7411 12
0.1%
7651 5
 
< 0.1%
7907 1
 
< 0.1%
8256 1
 
< 0.1%
8454 19
0.1%
12897 18
0.1%
14932 1
 
< 0.1%
22710 1
 
< 0.1%
ValueCountFrequency (%)
312696 1
 
< 0.1%
312654 2
 
< 0.1%
312651 2
 
< 0.1%
312608 1
 
< 0.1%
312432 2
 
< 0.1%
312384 1
 
< 0.1%
312241 1
 
< 0.1%
312159 5
< 0.1%
312026 1
 
< 0.1%
311981 1
 
< 0.1%

country_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size980.7 KiB
Kenya
15069 
Ghana
3525 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters92970
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKenya
2nd rowKenya
3rd rowKenya
4th rowKenya
5th rowKenya

Common Values

ValueCountFrequency (%)
Kenya 15069
81.0%
Ghana 3525
 
19.0%

Length

2025-01-06T20:11:59.330100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-06T20:11:59.438404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kenya 15069
81.0%
ghana 3525
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a 22119
23.8%
n 18594
20.0%
K 15069
16.2%
e 15069
16.2%
y 15069
16.2%
G 3525
 
3.8%
h 3525
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 22119
23.8%
n 18594
20.0%
K 15069
16.2%
e 15069
16.2%
y 15069
16.2%
G 3525
 
3.8%
h 3525
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 22119
23.8%
n 18594
20.0%
K 15069
16.2%
e 15069
16.2%
y 15069
16.2%
G 3525
 
3.8%
h 3525
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 22119
23.8%
n 18594
20.0%
K 15069
16.2%
e 15069
16.2%
y 15069
16.2%
G 3525
 
3.8%
h 3525
 
3.8%

tbl_loan_id
Real number (ℝ)

High correlation 

Distinct17067
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282416.63
Minimum104034
Maximum375320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:11:59.635965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum104034
5-th percentile217958.6
Q1240880.5
median273442.5
Q3304856
95-th percentile365207.35
Maximum375320
Range271286
Interquartile range (IQR)63975.5

Descriptive statistics

Standard deviation52907.549
Coefficient of variation (CV)0.18733864
Kurtosis-0.53525991
Mean282416.63
Median Absolute Deviation (MAD)31915
Skewness0.31085808
Sum5.2512549 × 109
Variance2.7992087 × 109
MonotonicityNot monotonic
2025-01-06T20:11:59.884557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
364300 3
 
< 0.1%
364043 3
 
< 0.1%
364217 3
 
< 0.1%
364013 3
 
< 0.1%
363955 3
 
< 0.1%
364044 3
 
< 0.1%
364014 3
 
< 0.1%
364301 3
 
< 0.1%
364358 3
 
< 0.1%
364297 3
 
< 0.1%
Other values (17057) 18564
99.8%
ValueCountFrequency (%)
104034 1
< 0.1%
104601 1
< 0.1%
104603 1
< 0.1%
105198 1
< 0.1%
105348 1
< 0.1%
105353 1
< 0.1%
105883 1
< 0.1%
105966 1
< 0.1%
106386 1
< 0.1%
110034 1
< 0.1%
ValueCountFrequency (%)
375320 1
< 0.1%
375315 1
< 0.1%
375306 1
< 0.1%
375304 2
< 0.1%
375289 1
< 0.1%
375288 2
< 0.1%
375286 1
< 0.1%
375280 1
< 0.1%
375277 1
< 0.1%
375275 1
< 0.1%

lender_id
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271876.75
Minimum245684
Maximum297183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:12:00.042824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum245684
5-th percentile267277
Q1267278
median267278
Q3267278
95-th percentile297183
Maximum297183
Range51499
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12349.646
Coefficient of variation (CV)0.045423693
Kurtosis0.5212782
Mean271876.75
Median Absolute Deviation (MAD)0
Skewness1.2138236
Sum5.0552763 × 109
Variance1.5251376 × 108
MonotonicityNot monotonic
2025-01-06T20:12:00.230693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
267278 14221
76.5%
296542 1803
 
9.7%
297183 1264
 
6.8%
251804 761
 
4.1%
296540 179
 
1.0%
297182 163
 
0.9%
245684 157
 
0.8%
267277 46
 
0.2%
ValueCountFrequency (%)
245684 157
 
0.8%
251804 761
 
4.1%
267277 46
 
0.2%
267278 14221
76.5%
296540 179
 
1.0%
296542 1803
 
9.7%
297182 163
 
0.9%
297183 1264
 
6.8%
ValueCountFrequency (%)
297183 1264
 
6.8%
297182 163
 
0.9%
296542 1803
 
9.7%
296540 179
 
1.0%
267278 14221
76.5%
267277 46
 
0.2%
251804 761
 
4.1%
245684 157
 
0.8%

loan_type
Categorical

High correlation  Imbalance 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size999.0 KiB
Type_1
13618 
Type_3
3039 
Type_7
 
592
Type_2
 
454
Type_5
 
298
Other values (17)
 
593

Length

Max length7
Median length6
Mean length6.0087663
Min length6

Characters and Unicode

Total characters111727
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowType_1
2nd rowType_1
3rd rowType_1
4th rowType_1
5th rowType_1

Common Values

ValueCountFrequency (%)
Type_1 13618
73.2%
Type_3 3039
 
16.3%
Type_7 592
 
3.2%
Type_2 454
 
2.4%
Type_5 298
 
1.6%
Type_4 253
 
1.4%
Type_6 98
 
0.5%
Type_10 96
 
0.5%
Type_9 42
 
0.2%
Type_8 37
 
0.2%
Other values (12) 67
 
0.4%

Length

2025-01-06T20:12:00.393121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
type_1 13618
73.2%
type_3 3039
 
16.3%
type_7 592
 
3.2%
type_2 454
 
2.4%
type_5 298
 
1.6%
type_4 253
 
1.4%
type_6 98
 
0.5%
type_10 96
 
0.5%
type_9 42
 
0.2%
type_8 37
 
0.2%
Other values (12) 67
 
0.4%

Most occurring characters

ValueCountFrequency (%)
T 18594
16.6%
y 18594
16.6%
p 18594
16.6%
e 18594
16.6%
_ 18594
16.6%
1 13783
12.3%
3 3047
 
2.7%
7 595
 
0.5%
2 473
 
0.4%
5 299
 
0.3%
Other values (5) 560
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 18594
16.6%
y 18594
16.6%
p 18594
16.6%
e 18594
16.6%
_ 18594
16.6%
1 13783
12.3%
3 3047
 
2.7%
7 595
 
0.5%
2 473
 
0.4%
5 299
 
0.3%
Other values (5) 560
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 18594
16.6%
y 18594
16.6%
p 18594
16.6%
e 18594
16.6%
_ 18594
16.6%
1 13783
12.3%
3 3047
 
2.7%
7 595
 
0.5%
2 473
 
0.4%
5 299
 
0.3%
Other values (5) 560
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 18594
16.6%
y 18594
16.6%
p 18594
16.6%
e 18594
16.6%
_ 18594
16.6%
1 13783
12.3%
3 3047
 
2.7%
7 595
 
0.5%
2 473
 
0.4%
5 299
 
0.3%
Other values (5) 560
 
0.5%

Total_Amount
Real number (ℝ)

High correlation  Skewed 

Distinct9372
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14465.074
Minimum5
Maximum20000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:12:00.638245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile719
Q12101.9
median4740
Q310267.75
95-th percentile50000
Maximum20000000
Range19999995
Interquartile range (IQR)8165.85

Descriptive statistics

Standard deviation156908.47
Coefficient of variation (CV)10.847402
Kurtosis14191.573
Mean14465.074
Median Absolute Deviation (MAD)3169
Skewness112.85841
Sum2.6896358 × 108
Variance2.4620266 × 1010
MonotonicityNot monotonic
2025-01-06T20:12:00.913397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 183
 
1.0%
5000 148
 
0.8%
10000 66
 
0.4%
2199 56
 
0.3%
4699 50
 
0.3%
2250 45
 
0.2%
2000 45
 
0.2%
2649 43
 
0.2%
6000 43
 
0.2%
6499 36
 
0.2%
Other values (9362) 17879
96.2%
ValueCountFrequency (%)
5 2
 
< 0.1%
10 5
< 0.1%
27 1
 
< 0.1%
30 1
 
< 0.1%
50 2
 
< 0.1%
70 1
 
< 0.1%
100 2
 
< 0.1%
109 1
 
< 0.1%
110 1
 
< 0.1%
112 1
 
< 0.1%
ValueCountFrequency (%)
20000000 1
 
< 0.1%
3986325 1
 
< 0.1%
3006566 1
 
< 0.1%
2263187 2
 
< 0.1%
1100000 1
 
< 0.1%
1000000 1
 
< 0.1%
837817.2 1
 
< 0.1%
810119.1 1
 
< 0.1%
800000 5
< 0.1%
790609 1
 
< 0.1%

Total_Amount_to_Repay
Real number (ℝ)

High correlation  Skewed 

Distinct10963
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15784.161
Minimum0
Maximum24152842
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:12:01.126149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile730
Q12164.48
median4828
Q310567.573
95-th percentile52990
Maximum24152842
Range24152842
Interquartile range (IQR)8403.0925

Descriptive statistics

Standard deviation187189.29
Coefficient of variation (CV)11.859312
Kurtosis14891.287
Mean15784.161
Median Absolute Deviation (MAD)3210
Skewness116.51743
Sum2.9349069 × 108
Variance3.5039829 × 1010
MonotonicityNot monotonic
2025-01-06T20:12:01.379693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5176 98
 
0.5%
1555 54
 
0.3%
1500 40
 
0.2%
2199 37
 
0.2%
4699 33
 
0.2%
2249 25
 
0.1%
10700 24
 
0.1%
2649 23
 
0.1%
6211 22
 
0.1%
2250 21
 
0.1%
Other values (10953) 18217
98.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.19 2
< 0.1%
6 2
< 0.1%
10.15 1
 
< 0.1%
10.7 1
 
< 0.1%
11 3
< 0.1%
30.45 1
 
< 0.1%
33.14 1
 
< 0.1%
52 1
 
< 0.1%
70 1
 
< 0.1%
ValueCountFrequency (%)
24152842 1
< 0.1%
4205572.88 1
< 0.1%
3167417.28 1
< 0.1%
2395583.44 2
< 0.1%
1240486 1
< 0.1%
1092000 1
< 0.1%
896050 2
< 0.1%
894873 1
< 0.1%
855316 2
< 0.1%
850384.46 1
< 0.1%
Distinct656
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size145.4 KiB
Minimum2021-11-08 00:00:00
Maximum2024-11-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-06T20:12:01.625872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:12:01.934914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct728
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size145.4 KiB
Minimum2021-11-15 00:00:00
Maximum2025-01-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-06T20:12:02.444684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:12:02.811332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

duration
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.530763
Minimum1
Maximum849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:12:03.053555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q17
median7
Q37
95-th percentile14
Maximum849
Range848
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.437325
Coefficient of variation (CV)2.6929247
Kurtosis60.061692
Mean13.530763
Median Absolute Deviation (MAD)0
Skewness7.0122942
Sum251591
Variance1327.6786
MonotonicityNot monotonic
2025-01-06T20:12:03.370296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 17365
93.4%
14 311
 
1.7%
180 290
 
1.6%
30 211
 
1.1%
90 56
 
0.3%
365 51
 
0.3%
240 39
 
0.2%
300 37
 
0.2%
60 33
 
0.2%
210 26
 
0.1%
Other values (40) 175
 
0.9%
ValueCountFrequency (%)
1 5
 
< 0.1%
3 4
 
< 0.1%
4 8
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 17365
93.4%
8 1
 
< 0.1%
14 311
 
1.7%
15 2
 
< 0.1%
20 2
 
< 0.1%
ValueCountFrequency (%)
849 1
 
< 0.1%
365 51
0.3%
360 9
 
< 0.1%
330 1
 
< 0.1%
300 37
0.2%
270 5
 
< 0.1%
265 3
 
< 0.1%
240 39
0.2%
210 26
0.1%
183 2
 
< 0.1%

New_versus_Repeat
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Repeat Loan
18425 
New Loan
 
169

Length

Max length11
Median length11
Mean length10.972733
Min length8

Characters and Unicode

Total characters204027
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRepeat Loan
2nd rowRepeat Loan
3rd rowRepeat Loan
4th rowRepeat Loan
5th rowRepeat Loan

Common Values

ValueCountFrequency (%)
Repeat Loan 18425
99.1%
New Loan 169
 
0.9%

Length

2025-01-06T20:12:03.601155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-06T20:12:03.748480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
loan 18594
50.0%
repeat 18425
49.5%
new 169
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 37019
18.1%
a 37019
18.1%
18594
9.1%
L 18594
9.1%
o 18594
9.1%
n 18594
9.1%
R 18425
9.0%
p 18425
9.0%
t 18425
9.0%
N 169
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 204027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 37019
18.1%
a 37019
18.1%
18594
9.1%
L 18594
9.1%
o 18594
9.1%
n 18594
9.1%
R 18425
9.0%
p 18425
9.0%
t 18425
9.0%
N 169
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 204027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 37019
18.1%
a 37019
18.1%
18594
9.1%
L 18594
9.1%
o 18594
9.1%
n 18594
9.1%
R 18425
9.0%
p 18425
9.0%
t 18425
9.0%
N 169
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 204027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 37019
18.1%
a 37019
18.1%
18594
9.1%
L 18594
9.1%
o 18594
9.1%
n 18594
9.1%
R 18425
9.0%
p 18425
9.0%
t 18425
9.0%
N 169
 
0.1%

Amount_Funded_By_Lender
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct9704
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2278.4301
Minimum0
Maximum400000
Zeros1868
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:12:03.938059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1239.36
median744.575
Q31998
95-th percentile9147.525
Maximum400000
Range400000
Interquartile range (IQR)1758.64

Descriptive statistics

Standard deviation6784.4298
Coefficient of variation (CV)2.9776773
Kurtosis848.23058
Mean2278.4301
Median Absolute Deviation (MAD)639.435
Skewness20.792936
Sum42365130
Variance46028487
MonotonicityNot monotonic
2025-01-06T20:12:04.204505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1868
 
10.0%
450 145
 
0.8%
1000 115
 
0.6%
1200 50
 
0.3%
659.7 43
 
0.2%
600 38
 
0.2%
1574.7 34
 
0.2%
1496.7 32
 
0.2%
10000 31
 
0.2%
179.7 29
 
0.2%
Other values (9694) 16209
87.2%
ValueCountFrequency (%)
0 1868
10.0%
0.01 11
 
0.1%
0.02 6
 
< 0.1%
0.03 2
 
< 0.1%
0.04 4
 
< 0.1%
0.05 3
 
< 0.1%
0.06 4
 
< 0.1%
0.07 4
 
< 0.1%
0.08 1
 
< 0.1%
0.09 3
 
< 0.1%
ValueCountFrequency (%)
400000 1
 
< 0.1%
216666 1
 
< 0.1%
200000 2
< 0.1%
190000 1
 
< 0.1%
140000 2
< 0.1%
119798.1 1
 
< 0.1%
100000 3
< 0.1%
98060.7 1
 
< 0.1%
97606 1
 
< 0.1%
93750 1
 
< 0.1%

Lender_portion_Funded
Real number (ℝ)

High correlation  Zeros 

Distinct3880
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20708981
Minimum0
Maximum1
Zeros1868
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:12:04.474581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13131313
median0.3
Q30.3
95-th percentile0.3
Maximum1
Range1
Interquartile range (IQR)0.16868687

Descriptive statistics

Standard deviation0.12208543
Coefficient of variation (CV)0.58952889
Kurtosis2.6668197
Mean0.20708981
Median Absolute Deviation (MAD)0.016608123
Skewness0.071617548
Sum3850.628
Variance0.014904852
MonotonicityNot monotonic
2025-01-06T20:12:04.697599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 9232
49.7%
0 1868
 
10.0%
0.2 721
 
3.9%
0.16 457
 
2.5%
0.1333333333 150
 
0.8%
0.13 150
 
0.8%
0.5 74
 
0.4%
0.1578947368 66
 
0.4%
0.1588785047 61
 
0.3%
0.1308411215 58
 
0.3%
Other values (3870) 5757
31.0%
ValueCountFrequency (%)
0 1868
10.0%
2.589533107 × 10-71
 
< 0.1%
4.528165187 × 10-71
 
< 0.1%
9.04077389 × 10-71
 
< 0.1%
1.036537963 × 10-61
 
< 0.1%
1.042807237 × 10-61
 
< 0.1%
1.294665976 × 10-61
 
< 0.1%
1.364628821 × 10-61
 
< 0.1%
1.724435247 × 10-61
 
< 0.1%
1.750087504 × 10-61
 
< 0.1%
ValueCountFrequency (%)
1 32
0.2%
0.9993333333 1
 
< 0.1%
0.9988766065 1
 
< 0.1%
0.9958333333 1
 
< 0.1%
0.95 1
 
< 0.1%
0.9415521064 1
 
< 0.1%
0.9090909091 1
 
< 0.1%
0.8911067546 1
 
< 0.1%
0.8 9
 
< 0.1%
0.75 1
 
< 0.1%

Lender_portion_to_be_repaid
Real number (ℝ)

High correlation  Zeros 

Distinct6782
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2466.452
Minimum0
Maximum423400
Zeros1953
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size145.4 KiB
2025-01-06T20:12:04.952663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1244.035
median758.92
Q32041
95-th percentile10146.734
Maximum423400
Range423400
Interquartile range (IQR)1796.965

Descriptive statistics

Standard deviation7680.0818
Coefficient of variation (CV)3.1138177
Kurtosis694.62888
Mean2466.452
Median Absolute Deviation (MAD)651.08
Skewness19.076854
Sum45861208
Variance58983657
MonotonicityNot monotonic
2025-01-06T20:12:05.204049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1953
 
10.5%
1035 95
 
0.5%
1 64
 
0.3%
2 46
 
0.2%
467 43
 
0.2%
4 42
 
0.2%
450 42
 
0.2%
675 34
 
0.2%
660 34
 
0.2%
3 32
 
0.2%
Other values (6772) 16209
87.2%
ValueCountFrequency (%)
0 1953
10.5%
1 64
 
0.3%
2 46
 
0.2%
3 32
 
0.2%
4 42
 
0.2%
5 25
 
0.1%
6 19
 
0.1%
7 14
 
0.1%
8 12
 
0.1%
9 15
 
0.1%
ValueCountFrequency (%)
423400 1
< 0.1%
236599 1
< 0.1%
223718 1
< 0.1%
216259 1
< 0.1%
210700 1
< 0.1%
184489.07 1
< 0.1%
152900 2
< 0.1%
125027.39 1
< 0.1%
123794.19 1
< 0.1%
107503 1
< 0.1%

Interactions

2025-01-06T20:11:54.802502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:41.162725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:42.864947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:44.458744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:45.966496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:47.984984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:49.736800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:51.409340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:53.218651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:54.990925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:41.381139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:43.021226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:44.614117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:46.180318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:48.176079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:49.929481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:51.610441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:53.441320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:55.165204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:41.596575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:43.214850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:44.781232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:46.364932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:48.350994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:50.114707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:51.801838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:53.632698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:55.548774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:41.781234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:43.381050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:44.947556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:46.761159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:48.546373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:50.289788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:52.009535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:53.795330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:55.754288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:41.997663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:43.581091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:45.130967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:46.968856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:48.701664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:50.497501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:52.253414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:53.956282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:55.958493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:42.164759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:43.747780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:45.283549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:47.172432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:48.875992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:50.696712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:52.443978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:54.140044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:56.114086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:42.347974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:43.944313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:45.479803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:47.386144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:49.082577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:50.878568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:52.630503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:54.282780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:56.324238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:42.593688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:44.137980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:45.640245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:47.625709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:49.351491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:51.069617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:52.804657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:54.505828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:56.536654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:42.747451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:44.314316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:45.807332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:47.835100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:49.549786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:51.244505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:53.027442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-06T20:11:54.645400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-06T20:12:05.374898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Amount_Funded_By_LenderLender_portion_FundedLender_portion_to_be_repaidNew_versus_RepeatTotal_AmountTotal_Amount_to_Repaycountry_idcustomer_iddurationlender_idloan_typetbl_loan_id
Amount_Funded_By_Lender1.0000.5670.9990.0000.6440.6440.018-0.0860.331-0.2010.246-0.157
Lender_portion_Funded0.5671.0000.5630.232-0.078-0.0790.717-0.232-0.105-0.2220.485-0.378
Lender_portion_to_be_repaid0.9990.5631.0000.0090.6450.6460.039-0.0810.340-0.1990.261-0.152
New_versus_Repeat0.0000.2320.0091.0000.0000.0000.0460.0960.0930.1520.3190.145
Total_Amount0.644-0.0780.6450.0001.0000.9990.000-0.1200.359-0.2410.912-0.091
Total_Amount_to_Repay0.644-0.0790.6460.0000.9991.0000.000-0.1150.363-0.2400.816-0.086
country_id0.0180.7170.0390.0460.0000.0001.0000.9130.3260.9950.9990.884
customer_id-0.086-0.232-0.0810.096-0.120-0.1150.9131.0000.0690.5030.3440.553
duration0.331-0.1050.3400.0930.3590.3630.3260.0691.000-0.1440.6660.111
lender_id-0.201-0.222-0.1990.152-0.241-0.2400.9950.503-0.1441.0000.8010.483
loan_type0.2460.4850.2610.3190.9120.8160.9990.3440.6660.8011.0000.456
tbl_loan_id-0.157-0.378-0.1520.145-0.091-0.0860.8840.5530.1110.4830.4561.000

Missing values

2025-01-06T20:11:56.815702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-06T20:11:57.092367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDcustomer_idcountry_idtbl_loan_idlender_idloan_typeTotal_AmountTotal_Amount_to_Repaydisbursement_datedue_datedurationNew_versus_RepeatAmount_Funded_By_LenderLender_portion_FundedLender_portion_to_be_repaid
0ID_269404226088267278269404Kenya226088267278Type_11919.01989.02022-07-272022-08-037Repeat Loan575.70.300000597.0
1ID_255356300042267278255356Kenya300042267278Type_12138.02153.02022-11-162022-11-237Repeat Loan0.00.0000000.0
2ID_257026243764267278257026Kenya243764267278Type_18254.08304.02022-08-242022-08-317Repeat Loan207.00.025079208.0
3ID_264617299409267278264617Kenya299409267278Type_13379.03379.02022-11-152022-11-227Repeat Loan1013.70.3000001014.0
4ID_247613296713267278247613Kenya296713267278Type_1120.0120.02022-11-102022-11-177Repeat Loan36.00.30000036.0
5ID_271847294122267278271847Kenya294122267278Type_13438.03471.02022-11-052022-11-127Repeat Loan1031.40.3000001041.0
6ID_308399367770267278308399Kenya367770267278Type_75000.05181.02024-07-172024-07-247Repeat Loan1000.00.2000001036.0
7ID_253278278418267278253278Kenya278418267278Type_13917.03917.02022-10-102022-10-177Repeat Loan117.00.029870117.0
8ID_256877248892267278256877Kenya248892267278Type_14799.04799.02022-08-312022-09-077Repeat Loan0.00.0000000.0
9ID_262156246268267278262156Kenya246268267278Type_15708.05885.02022-08-272022-09-037Repeat Loan120.00.021023124.0
IDcustomer_idcountry_idtbl_loan_idlender_idloan_typeTotal_AmountTotal_Amount_to_Repaydisbursement_datedue_datedurationNew_versus_RepeatAmount_Funded_By_LenderLender_portion_FundedLender_portion_to_be_repaid
18584ID_269603285181267278269603Kenya285181267278Type_13139.003181.002022-10-202022-10-277Repeat Loan0.000.0000000.00
18585ID_268168247705267278268168Kenya247705267278Type_19539.009765.002022-08-302022-09-067Repeat Loan0.000.0000000.00
18586ID_251462269485267278251462Kenya269485267278Type_14399.004399.002022-09-282022-10-057Repeat Loan124.050.028200124.00
18587ID_260626298889267278260626Kenya298889267278Type_14648.004681.002022-11-142022-11-217Repeat Loan1394.400.3000001404.00
18588ID_246989262183267278246989Kenya262183267278Type_550000.0052600.002022-09-192022-10-0314Repeat Loan8000.000.1600008416.00
18589ID_297596365331297183297596Ghana365331297183Type_31730.411782.322023-02-092023-02-167Repeat Loan269.410.155689279.77
18590ID_259715231897267278259715Kenya231897267278Type_11534.001534.002022-08-042022-08-117Repeat Loan460.200.300000460.00
18591ID_296701364008297183296701Ghana364008297183Type_31372.211413.302022-06-232022-06-307Repeat Loan178.670.130208178.67
18592ID_268271242864267278268271Kenya242864267278Type_15608.005781.002022-08-232022-08-307Repeat Loan0.000.0000000.00
18593ID_248929241821267278248929Kenya241821267278Type_14038.004038.002022-08-222022-08-297Repeat Loan0.000.0000000.00